Overview

Dataset statistics

Number of variables26
Number of observations193
Missing cells1071
Missing cells (%)21.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.7 KiB
Average record size in memory216.0 B

Variable types

Categorical2
Numeric24

Alerts

code has a high cardinality: 193 distinct values High cardinality
country has a high cardinality: 193 distinct values High cardinality
1st_const_year is highly correlated with 2nd_const_year and 7 other fieldsHigh correlation
2nd_const_year is highly correlated with 1st_const_year and 6 other fieldsHigh correlation
1st_2nd_tfidf is highly correlated with 1st_current_tfidf and 2 other fieldsHigh correlation
1st_current_tfidf is highly correlated with 1st_2nd_tfidf and 7 other fieldsHigh correlation
1st_2nd_lda is highly correlated with 1st_2nd_tfidf and 2 other fieldsHigh correlation
1st_current_lda is highly correlated with 1st_current_tfidf and 3 other fieldsHigh correlation
1st_2nd_use is highly correlated with 1st_2nd_lda and 4 other fieldsHigh correlation
1st_current_use is highly correlated with 1st_2nd_use and 2 other fieldsHigh correlation
1st_2nd_stm is highly correlated with 1st_2nd_lda and 1 other fieldsHigh correlation
1st_current_stm is highly correlated with 1st_current_tfidf and 4 other fieldsHigh correlation
constitutional_time is highly correlated with 1st_const_year and 7 other fieldsHigh correlation
first_regime_time is highly correlated with 1st_2nd_tfidf_adj and 3 other fieldsHigh correlation
1st_2nd_tfidf_adj is highly correlated with first_regime_time and 4 other fieldsHigh correlation
1st_2nd_lda_adj is highly correlated with first_regime_time and 3 other fieldsHigh correlation
1st_2nd_use_adj is highly correlated with 1st_2nd_use and 5 other fieldsHigh correlation
1st_2nd_stm_adj is highly correlated with first_regime_time and 3 other fieldsHigh correlation
1st_curr_tfidf_adj is highly correlated with 1st_current_tfidf and 3 other fieldsHigh correlation
1st_curr_lda_adj is highly correlated with 1st_const_year and 5 other fieldsHigh correlation
1st_curr_use_adj is highly correlated with 1st_current_use and 3 other fieldsHigh correlation
1st_curr_stm_adj is highly correlated with 1st_const_year and 5 other fieldsHigh correlation
tfidf_distance is highly correlated with 1st_const_year and 9 other fieldsHigh correlation
lda_distance is highly correlated with 1st_const_year and 8 other fieldsHigh correlation
use_distance is highly correlated with 1st_const_year and 8 other fieldsHigh correlation
stm_distance is highly correlated with 1st_const_year and 8 other fieldsHigh correlation
1st_const_year is highly correlated with 2nd_const_year and 6 other fieldsHigh correlation
2nd_const_year is highly correlated with 1st_const_year and 6 other fieldsHigh correlation
1st_2nd_tfidf is highly correlated with 1st_current_tfidf and 1 other fieldsHigh correlation
1st_current_tfidf is highly correlated with 1st_2nd_tfidf and 3 other fieldsHigh correlation
1st_2nd_lda is highly correlated with 1st_2nd_tfidf and 3 other fieldsHigh correlation
1st_current_lda is highly correlated with 1st_current_tfidf and 3 other fieldsHigh correlation
1st_2nd_use is highly correlated with 1st_2nd_lda and 2 other fieldsHigh correlation
1st_current_use is highly correlated with 1st_2nd_use and 1 other fieldsHigh correlation
1st_2nd_stm is highly correlated with 1st_2nd_lda and 1 other fieldsHigh correlation
1st_current_stm is highly correlated with 1st_current_tfidf and 4 other fieldsHigh correlation
constitutional_time is highly correlated with 1st_const_year and 6 other fieldsHigh correlation
1st_2nd_tfidf_adj is highly correlated with 1st_2nd_lda_adj and 2 other fieldsHigh correlation
1st_2nd_lda_adj is highly correlated with 1st_2nd_tfidf_adj and 2 other fieldsHigh correlation
1st_2nd_use_adj is highly correlated with 1st_2nd_tfidf_adj and 2 other fieldsHigh correlation
1st_2nd_stm_adj is highly correlated with 1st_2nd_tfidf_adj and 2 other fieldsHigh correlation
1st_curr_tfidf_adj is highly correlated with 1st_current_tfidf and 2 other fieldsHigh correlation
1st_curr_lda_adj is highly correlated with 1st_const_year and 5 other fieldsHigh correlation
1st_curr_use_adj is highly correlated with 1st_2nd_use and 2 other fieldsHigh correlation
1st_curr_stm_adj is highly correlated with 1st_const_year and 4 other fieldsHigh correlation
tfidf_distance is highly correlated with 1st_const_year and 5 other fieldsHigh correlation
lda_distance is highly correlated with 1st_const_year and 7 other fieldsHigh correlation
use_distance is highly correlated with tfidf_distance and 2 other fieldsHigh correlation
stm_distance is highly correlated with 1st_const_year and 6 other fieldsHigh correlation
1st_const_year is highly correlated with 2nd_const_year and 2 other fieldsHigh correlation
2nd_const_year is highly correlated with 1st_const_year and 1 other fieldsHigh correlation
1st_2nd_tfidf is highly correlated with 1st_current_tfidfHigh correlation
1st_current_tfidf is highly correlated with 1st_2nd_tfidfHigh correlation
1st_2nd_lda is highly correlated with 1st_2nd_stmHigh correlation
1st_current_lda is highly correlated with 1st_current_stmHigh correlation
1st_current_use is highly correlated with 1st_curr_use_adj and 1 other fieldsHigh correlation
1st_2nd_stm is highly correlated with 1st_2nd_ldaHigh correlation
1st_current_stm is highly correlated with 1st_current_lda and 1 other fieldsHigh correlation
constitutional_time is highly correlated with 1st_const_year and 2 other fieldsHigh correlation
first_regime_time is highly correlated with 1st_2nd_tfidf_adj and 2 other fieldsHigh correlation
1st_2nd_tfidf_adj is highly correlated with first_regime_time and 2 other fieldsHigh correlation
1st_2nd_lda_adj is highly correlated with first_regime_time and 3 other fieldsHigh correlation
1st_2nd_use_adj is highly correlated with 1st_2nd_lda_adj and 1 other fieldsHigh correlation
1st_2nd_stm_adj is highly correlated with first_regime_time and 3 other fieldsHigh correlation
1st_curr_tfidf_adj is highly correlated with 1st_curr_lda_adjHigh correlation
1st_curr_lda_adj is highly correlated with 1st_const_year and 3 other fieldsHigh correlation
1st_curr_use_adj is highly correlated with 1st_current_useHigh correlation
1st_curr_stm_adj is highly correlated with 1st_curr_lda_adjHigh correlation
tfidf_distance is highly correlated with lda_distance and 2 other fieldsHigh correlation
lda_distance is highly correlated with tfidf_distance and 2 other fieldsHigh correlation
use_distance is highly correlated with 1st_current_use and 3 other fieldsHigh correlation
stm_distance is highly correlated with 1st_current_stm and 3 other fieldsHigh correlation
1st_const_year is highly correlated with 2nd_const_year and 6 other fieldsHigh correlation
2nd_const_year is highly correlated with 1st_const_year and 7 other fieldsHigh correlation
1st_2nd_tfidf is highly correlated with 1st_current_tfidf and 1 other fieldsHigh correlation
1st_current_tfidf is highly correlated with 1st_2nd_tfidf and 3 other fieldsHigh correlation
1st_2nd_lda is highly correlated with 1st_2nd_tfidf and 4 other fieldsHigh correlation
1st_current_lda is highly correlated with 1st_current_tfidf and 3 other fieldsHigh correlation
1st_2nd_use is highly correlated with 1st_current_use and 4 other fieldsHigh correlation
1st_current_use is highly correlated with 1st_2nd_use and 3 other fieldsHigh correlation
1st_2nd_stm is highly correlated with 1st_2nd_lda and 2 other fieldsHigh correlation
1st_current_stm is highly correlated with 1st_current_tfidf and 4 other fieldsHigh correlation
constitutional_time is highly correlated with 1st_const_year and 6 other fieldsHigh correlation
first_regime_time is highly correlated with 1st_const_year and 1 other fieldsHigh correlation
1st_2nd_tfidf_adj is highly correlated with 2nd_const_year and 7 other fieldsHigh correlation
1st_2nd_lda_adj is highly correlated with 2nd_const_year and 6 other fieldsHigh correlation
1st_2nd_use_adj is highly correlated with 1st_2nd_use and 7 other fieldsHigh correlation
1st_2nd_stm_adj is highly correlated with 1st_2nd_tfidf_adj and 3 other fieldsHigh correlation
1st_curr_tfidf_adj is highly correlated with 1st_const_year and 5 other fieldsHigh correlation
1st_curr_lda_adj is highly correlated with 1st_const_year and 4 other fieldsHigh correlation
1st_curr_use_adj is highly correlated with 1st_2nd_use and 4 other fieldsHigh correlation
1st_curr_stm_adj is highly correlated with 1st_const_year and 5 other fieldsHigh correlation
tfidf_distance is highly correlated with 2nd_const_year and 4 other fieldsHigh correlation
lda_distance is highly correlated with 1st_const_year and 10 other fieldsHigh correlation
use_distance is highly correlated with 2nd_const_year and 6 other fieldsHigh correlation
stm_distance is highly correlated with 2nd_const_year and 3 other fieldsHigh correlation
2nd_const_year has 63 (32.6%) missing values Missing
1st_2nd_tfidf has 63 (32.6%) missing values Missing
1st_current_tfidf has 63 (32.6%) missing values Missing
1st_2nd_lda has 63 (32.6%) missing values Missing
1st_current_lda has 63 (32.6%) missing values Missing
1st_2nd_use has 63 (32.6%) missing values Missing
1st_current_use has 63 (32.6%) missing values Missing
1st_2nd_stm has 63 (32.6%) missing values Missing
1st_current_stm has 63 (32.6%) missing values Missing
1st_2nd_tfidf_adj has 63 (32.6%) missing values Missing
1st_2nd_lda_adj has 63 (32.6%) missing values Missing
1st_2nd_use_adj has 63 (32.6%) missing values Missing
1st_2nd_stm_adj has 63 (32.6%) missing values Missing
1st_curr_tfidf_adj has 63 (32.6%) missing values Missing
1st_curr_lda_adj has 63 (32.6%) missing values Missing
1st_curr_use_adj has 63 (32.6%) missing values Missing
1st_curr_stm_adj has 63 (32.6%) missing values Missing
code is uniformly distributed Uniform
country is uniformly distributed Uniform
code has unique values Unique
country has unique values Unique
tfidf_distance has 63 (32.6%) zeros Zeros
lda_distance has 63 (32.6%) zeros Zeros
use_distance has 63 (32.6%) zeros Zeros
stm_distance has 63 (32.6%) zeros Zeros

Reproduction

Analysis started2022-06-15 18:46:34.245667
Analysis finished2022-06-15 18:55:55.824472
Duration9 minutes and 21.58 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
AFG
 
1
LIE
 
1
NZL
 
1
NIC
 
1
NER
 
1
Other values (188)
188 

Length

Max length4
Median length3
Mean length3.005181347
Min length3

Characters and Unicode

Total characters580
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique193 ?
Unique (%)100.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowAND
5th rowAGO

Common Values

ValueCountFrequency (%)
AFG1
 
0.5%
LIE1
 
0.5%
NZL1
 
0.5%
NIC1
 
0.5%
NER1
 
0.5%
NGA1
 
0.5%
PRK1
 
0.5%
NOR1
 
0.5%
OMN1
 
0.5%
PAK1
 
0.5%
Other values (183)183
94.8%

Length

2022-06-15T15:55:56.603478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afg1
 
0.5%
alb1
 
0.5%
bhs1
 
0.5%
dza1
 
0.5%
and1
 
0.5%
ago1
 
0.5%
atg1
 
0.5%
arg1
 
0.5%
arm1
 
0.5%
aus1
 
0.5%
Other values (183)183
94.8%

Most occurring characters

ValueCountFrequency (%)
A44
 
7.6%
R43
 
7.4%
N40
 
6.9%
M36
 
6.2%
L34
 
5.9%
S33
 
5.7%
T32
 
5.5%
G29
 
5.0%
B28
 
4.8%
E26
 
4.5%
Other values (16)235
40.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter580
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A44
 
7.6%
R43
 
7.4%
N40
 
6.9%
M36
 
6.2%
L34
 
5.9%
S33
 
5.7%
T32
 
5.5%
G29
 
5.0%
B28
 
4.8%
E26
 
4.5%
Other values (16)235
40.5%

Most occurring scripts

ValueCountFrequency (%)
Latin580
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A44
 
7.6%
R43
 
7.4%
N40
 
6.9%
M36
 
6.2%
L34
 
5.9%
S33
 
5.7%
T32
 
5.5%
G29
 
5.0%
B28
 
4.8%
E26
 
4.5%
Other values (16)235
40.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A44
 
7.6%
R43
 
7.4%
N40
 
6.9%
M36
 
6.2%
L34
 
5.9%
S33
 
5.7%
T32
 
5.5%
G29
 
5.0%
B28
 
4.8%
E26
 
4.5%
Other values (16)235
40.5%

country
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Afghanistan
 
1
Liechtenstein
 
1
New zealand
 
1
Nicaragua
 
1
Niger
 
1
Other values (188)
188 

Length

Max length32
Median length21
Mean length8.455958549
Min length4

Characters and Unicode

Total characters1632
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique193 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola

Common Values

ValueCountFrequency (%)
Afghanistan1
 
0.5%
Liechtenstein1
 
0.5%
New zealand1
 
0.5%
Nicaragua1
 
0.5%
Niger1
 
0.5%
Nigeria1
 
0.5%
North Korea1
 
0.5%
Norway1
 
0.5%
Oman1
 
0.5%
Pakistan1
 
0.5%
Other values (183)183
94.8%

Length

2022-06-15T15:55:58.192004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic5
 
2.1%
and4
 
1.7%
guinea4
 
1.7%
saint3
 
1.2%
south3
 
1.2%
the2
 
0.8%
korea2
 
0.8%
united2
 
0.8%
sudan2
 
0.8%
arab2
 
0.8%
Other values (207)211
87.9%

Most occurring characters

ValueCountFrequency (%)
a255
15.6%
i144
 
8.8%
n125
 
7.7%
e109
 
6.7%
o90
 
5.5%
r89
 
5.5%
u66
 
4.0%
t62
 
3.8%
l60
 
3.7%
s52
 
3.2%
Other values (41)580
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1352
82.8%
Uppercase Letter233
 
14.3%
Space Separator47
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a255
18.9%
i144
10.7%
n125
9.2%
e109
 
8.1%
o90
 
6.7%
r89
 
6.6%
u66
 
4.9%
t62
 
4.6%
l60
 
4.4%
s52
 
3.8%
Other values (16)300
22.2%
Uppercase Letter
ValueCountFrequency (%)
S27
 
11.6%
M19
 
8.2%
B18
 
7.7%
C18
 
7.7%
A17
 
7.3%
T15
 
6.4%
G15
 
6.4%
N12
 
5.2%
L12
 
5.2%
I10
 
4.3%
Other values (14)70
30.0%
Space Separator
ValueCountFrequency (%)
47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1585
97.1%
Common47
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a255
16.1%
i144
 
9.1%
n125
 
7.9%
e109
 
6.9%
o90
 
5.7%
r89
 
5.6%
u66
 
4.2%
t62
 
3.9%
l60
 
3.8%
s52
 
3.3%
Other values (40)533
33.6%
Common
ValueCountFrequency (%)
47
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a255
15.6%
i144
 
8.8%
n125
 
7.7%
e109
 
6.7%
o90
 
5.5%
r89
 
5.5%
u66
 
4.0%
t62
 
3.8%
l60
 
3.7%
s52
 
3.2%
Other values (41)580
35.5%

1st_const_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct99
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1935.792746
Minimum1789
Maximum2011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:00.161542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1789
5-th percentile1816.4
Q11919
median1960
Q31975
95-th percentile1995
Maximum2011
Range222
Interquartile range (IQR)56

Descriptive statistics

Standard deviation57.59212321
Coefficient of variation (CV)0.02975118247
Kurtosis0.1093821862
Mean1935.792746
Median Absolute Deviation (MAD)21
Skewness-1.139208249
Sum373608
Variance3316.852655
MonotonicityNot monotonic
2022-06-15T15:56:01.934072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19609
 
4.7%
19628
 
4.1%
19786
 
3.1%
19475
 
2.6%
19915
 
2.6%
19795
 
2.6%
19645
 
2.6%
19815
 
2.6%
19755
 
2.6%
19615
 
2.6%
Other values (89)135
69.9%
ValueCountFrequency (%)
17891
0.5%
17912
1.0%
17951
0.5%
18011
0.5%
18081
0.5%
18091
0.5%
18111
0.5%
18131
0.5%
18141
0.5%
18181
0.5%
ValueCountFrequency (%)
20111
 
0.5%
20081
 
0.5%
20031
 
0.5%
20021
 
0.5%
19981
 
0.5%
19971
 
0.5%
19962
1.0%
19954
2.1%
19942
1.0%
19933
1.6%

2nd_const_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct83
Distinct (%)63.8%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean1950.130769
Minimum1793
Maximum2011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:03.440070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1793
5-th percentile1828.9
Q11931
median1969.5
Q31989.75
95-th percentile2007.55
Maximum2011
Range218
Interquartile range (IQR)58.75

Descriptive statistics

Standard deviation53.9311057
Coefficient of variation (CV)0.0276551227
Kurtosis0.7794133577
Mean1950.130769
Median Absolute Deviation (MAD)24.5
Skewness-1.284927154
Sum253517
Variance2908.564162
MonotonicityNot monotonic
2022-06-15T15:56:05.153210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19794
 
2.1%
19924
 
2.1%
19624
 
2.1%
19743
 
1.6%
19963
 
1.6%
20053
 
1.6%
19703
 
1.6%
20083
 
1.6%
19783
 
1.6%
19592
 
1.0%
Other values (73)98
50.8%
(Missing)63
32.6%
ValueCountFrequency (%)
17931
0.5%
18051
0.5%
18121
0.5%
18151
0.5%
18231
0.5%
18261
0.5%
18281
0.5%
18301
0.5%
18311
0.5%
18361
0.5%
ValueCountFrequency (%)
20112
1.0%
20102
1.0%
20083
1.6%
20071
 
0.5%
20053
1.6%
20022
1.0%
20012
1.0%
19991
 
0.5%
19981
 
0.5%
19963
1.6%

1st_2nd_tfidf
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.5546450492
Minimum0.0201733
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:06.519348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0201733
5-th percentile0.067218377
Q10.3511306875
median0.57103248
Q30.826286805
95-th percentile0.961606845
Maximum1
Range0.9798267
Interquartile range (IQR)0.4751561175

Descriptive statistics

Standard deviation0.2897924935
Coefficient of variation (CV)0.5224827913
Kurtosis-1.039943357
Mean0.5546450492
Median Absolute Deviation (MAD)0.241428725
Skewness-0.2278169548
Sum72.1038564
Variance0.08397968929
MonotonicityNot monotonic
2022-06-15T15:56:07.666524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35363041
 
0.5%
0.393748641
 
0.5%
0.982848721
 
0.5%
0.570171421
 
0.5%
0.895085461
 
0.5%
0.03000091
 
0.5%
0.775032861
 
0.5%
0.145779671
 
0.5%
0.809577551
 
0.5%
0.64141861
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.02017331
0.5%
0.03000091
0.5%
0.031100751
0.5%
0.043671131
0.5%
0.05659191
0.5%
0.05692411
0.5%
0.064511241
0.5%
0.07052711
0.5%
0.07055581
0.5%
0.081497851
0.5%
ValueCountFrequency (%)
11
0.5%
0.99762562171
0.5%
0.982848721
0.5%
0.9769401081
0.5%
0.9746597281
0.5%
0.9681447331
0.5%
0.9626884241
0.5%
0.9602849151
0.5%
0.951034361
0.5%
0.948841681
0.5%

1st_current_tfidf
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.6039565952
Minimum0.0201733
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:08.930601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0201733
5-th percentile0.0761215455
Q10.372131245
median0.64427824
Q30.8753182905
95-th percentile0.9830993891
Maximum1
Range0.9798267
Interquartile range (IQR)0.5031870455

Descriptive statistics

Standard deviation0.2953947897
Coefficient of variation (CV)0.4890993691
Kurtosis-1.025800126
Mean0.6039565952
Median Absolute Deviation (MAD)0.247694305
Skewness-0.4713303774
Sum78.51435738
Variance0.08725808177
MonotonicityNot monotonic
2022-06-15T15:56:10.514669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.30351681
 
0.5%
0.49961741
 
0.5%
0.982848721
 
0.5%
0.743672341
 
0.5%
0.739041031
 
0.5%
0.23950391
 
0.5%
0.47612981
 
0.5%
0.14458261
 
0.5%
0.9216653851
 
0.5%
0.9833044821
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.02017331
0.5%
0.031100751
0.5%
0.05659191
0.5%
0.05692411
0.5%
0.06050061
0.5%
0.064905051
0.5%
0.070498351
0.5%
0.082994341
0.5%
0.083840971
0.5%
0.086891831
0.5%
ValueCountFrequency (%)
11
0.5%
0.99762562171
0.5%
0.99365411561
0.5%
0.9902132371
0.5%
0.984260321
0.5%
0.984100111
0.5%
0.9833044821
0.5%
0.982848721
0.5%
0.9769401081
0.5%
0.964777961
0.5%

1st_2nd_lda
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.443165611
Minimum0.0009248798472
Maximum0.8324159977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:11.824810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0009248798472
5-th percentile0.1120276952
Q10.2944028361
median0.4365144665
Q30.5777939292
95-th percentile0.8067003927
Maximum0.8324159977
Range0.8314911179
Interquartile range (IQR)0.283391093

Descriptive statistics

Standard deviation0.2031193633
Coefficient of variation (CV)0.4583373761
Kurtosis-0.6423280871
Mean0.443165611
Median Absolute Deviation (MAD)0.1429888014
Skewness0.04752162724
Sum57.61152943
Variance0.04125747575
MonotonicityNot monotonic
2022-06-15T15:56:13.361465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.49020394771
 
0.5%
0.42470565171
 
0.5%
0.73381110111
 
0.5%
0.18325122371
 
0.5%
0.56998601481
 
0.5%
0.051539211361
 
0.5%
0.18375210321
 
0.5%
0.10902148831
 
0.5%
0.64532133291
 
0.5%
0.66592185071
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.00092487984721
0.5%
0.0090094242091
0.5%
0.024917354921
0.5%
0.051539211361
0.5%
0.1065684841
0.5%
0.10902148831
0.5%
0.11070008231
0.5%
0.11365033321
0.5%
0.12244263521
0.5%
0.15704539031
0.5%
ValueCountFrequency (%)
0.83241599771
0.5%
0.83224133631
0.5%
0.8313224891
0.5%
0.82477135241
0.5%
0.81508259041
0.5%
0.81122448641
0.5%
0.8108473991
0.5%
0.80163182931
0.5%
0.7773782541
0.5%
0.73981059081
0.5%

1st_current_lda
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.5835165935
Minimum0.009009424209
Maximum0.8324257359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:14.693468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.009009424209
5-th percentile0.2251729199
Q10.4804396153
median0.6170690393
Q30.731420406
95-th percentile0.8179054265
Maximum0.8324257359
Range0.8234163117
Interquartile range (IQR)0.2509807907

Descriptive statistics

Standard deviation0.1909343338
Coefficient of variation (CV)0.3272132035
Kurtosis0.277182096
Mean0.5835165935
Median Absolute Deviation (MAD)0.119330462
Skewness-0.8797226781
Sum75.85715715
Variance0.03645591984
MonotonicityNot monotonic
2022-06-15T15:56:16.207008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.63414851781
 
0.5%
0.54047192961
 
0.5%
0.73381110111
 
0.5%
0.67204730461
 
0.5%
0.74316318031
 
0.5%
0.41678333641
 
0.5%
0.22563521771
 
0.5%
0.047738257381
 
0.5%
0.83121224511
 
0.5%
0.80221496261
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.0090094242091
0.5%
0.047738257381
0.5%
0.1065684841
0.5%
0.11365033321
0.5%
0.14233390591
0.5%
0.14826868521
0.5%
0.22479467631
0.5%
0.22563521771
0.5%
0.26322877171
0.5%
0.27030336661
0.5%
ValueCountFrequency (%)
0.83242573591
0.5%
0.83241599771
0.5%
0.83218976871
0.5%
0.83121224511
0.5%
0.82467949451
0.5%
0.82133217061
0.5%
0.82021501961
0.5%
0.81508259041
0.5%
0.81317712011
0.5%
0.81122448641
0.5%

1st_2nd_use
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.1056574339
Minimum0.005343358713
Maximum0.4208145276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:17.590013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.005343358713
5-th percentile0.01089700337
Q10.03987112141
median0.06771642546
Q30.157467436
95-th percentile0.3063234332
Maximum0.4208145276
Range0.4154711689
Interquartile range (IQR)0.1175963146

Descriptive statistics

Standard deviation0.09324338942
Coefficient of variation (CV)0.8825066631
Kurtosis1.081367524
Mean0.1056574339
Median Absolute Deviation (MAD)0.03885217586
Skewness1.322426056
Sum13.73546641
Variance0.008694329671
MonotonicityNot monotonic
2022-06-15T15:56:18.771076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.055123293851
 
0.5%
0.050996769581
 
0.5%
0.32859160331
 
0.5%
0.010886710131
 
0.5%
0.042170748021
 
0.5%
0.007157855131
 
0.5%
0.052735268161
 
0.5%
0.013156904761
 
0.5%
0.27562074371
 
0.5%
0.30810189931
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.0053433587131
0.5%
0.0065091507551
0.5%
0.006937607521
0.5%
0.0070834480611
0.5%
0.007157855131
0.5%
0.010045618371
0.5%
0.010886710131
0.5%
0.0109095841
0.5%
0.013156904761
0.5%
0.014584934511
0.5%
ValueCountFrequency (%)
0.42081452761
0.5%
0.38670309931
0.5%
0.36421544691
0.5%
0.32859160331
0.5%
0.31137303621
0.5%
0.30810189931
0.5%
0.30706963131
0.5%
0.30541141341
0.5%
0.30092661051
0.5%
0.30057658421
0.5%

1st_current_use
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.1435744226
Minimum0.004723398218
Maximum0.5232621873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:30.571072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.004723398218
5-th percentile0.02655054297
Q10.07085178409
median0.1205537431
Q30.1908757909
95-th percentile0.3132243149
Maximum0.5232621873
Range0.5185387891
Interquartile range (IQR)0.1200240068

Descriptive statistics

Standard deviation0.09983241559
Coefficient of variation (CV)0.6953356578
Kurtosis1.864928993
Mean0.1435744226
Median Absolute Deviation (MAD)0.05930442002
Skewness1.24056138
Sum18.66467494
Variance0.009966511202
MonotonicityNot monotonic
2022-06-15T15:56:32.044072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.16696404411
 
0.5%
0.086873777791
 
0.5%
0.32859160331
 
0.5%
0.086846111841
 
0.5%
0.15123873811
 
0.5%
0.071665196381
 
0.5%
0.091275170871
 
0.5%
0.008969030611
 
0.5%
0.50299650221
 
0.5%
0.10787741171
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.0047233982181
0.5%
0.0053433587131
0.5%
0.008969030611
0.5%
0.0095469104051
0.5%
0.0109095841
0.5%
0.013499296131
0.5%
0.022008609711
0.5%
0.032101794731
0.5%
0.033307477691
0.5%
0.033643620661
0.5%
ValueCountFrequency (%)
0.52326218731
0.5%
0.50299650221
0.5%
0.42081452761
0.5%
0.36421544691
0.5%
0.33801860791
0.5%
0.32859160331
0.5%
0.31433921961
0.5%
0.31186165371
0.5%
0.30706963131
0.5%
0.30570881181
0.5%

1st_2nd_stm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.4255534369
Minimum0.05346783526
Maximum0.8310721109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:33.488075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.05346783526
5-th percentile0.1345424176
Q10.2675765435
median0.3944332318
Q30.57040201
95-th percentile0.7747892121
Maximum0.8310721109
Range0.7776042756
Interquartile range (IQR)0.3028254664

Descriptive statistics

Standard deviation0.1973448023
Coefficient of variation (CV)0.4637368312
Kurtosis-0.7112521818
Mean0.4255534369
Median Absolute Deviation (MAD)0.1363291275
Skewness0.3402544825
Sum55.3219468
Variance0.03894497101
MonotonicityNot monotonic
2022-06-15T15:56:34.685073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.18080946331
 
0.5%
0.34018463721
 
0.5%
0.79270690661
 
0.5%
0.25976748391
 
0.5%
0.10863747281
 
0.5%
0.27513063341
 
0.5%
0.14267082581
 
0.5%
0.23710905921
 
0.5%
0.4966865971
 
0.5%
0.76358743961
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.053467835261
0.5%
0.063017319071
0.5%
0.066646754721
0.5%
0.091930175211
0.5%
0.1039359941
0.5%
0.10863747281
0.5%
0.12789190171
0.5%
0.14267082581
0.5%
0.16748033791
0.5%
0.17954269321
0.5%
ValueCountFrequency (%)
0.83107211091
0.5%
0.83026234881
0.5%
0.82203120781
0.5%
0.81393198741
0.5%
0.81298556651
0.5%
0.79270690661
0.5%
0.77507106251
0.5%
0.77444472831
0.5%
0.76466348191
0.5%
0.76367544621
0.5%

1st_current_stm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.5528050629
Minimum0.06664675472
Maximum0.8312656847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:36.003602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.06664675472
5-th percentile0.207770493
Q10.3816559025
median0.5801652868
Q30.7475594909
95-th percentile0.8257300116
Maximum0.8312656847
Range0.7646189299
Interquartile range (IQR)0.3659035884

Descriptive statistics

Standard deviation0.2059741367
Coefficient of variation (CV)0.3725981371
Kurtosis-1.016112254
Mean0.5528050629
Median Absolute Deviation (MAD)0.188419597
Skewness-0.3125395666
Sum71.86465818
Variance0.04242534497
MonotonicityNot monotonic
2022-06-15T15:56:37.126692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35145796091
 
0.5%
0.65991272991
 
0.5%
0.79270690661
 
0.5%
0.79229897021
 
0.5%
0.83126568471
 
0.5%
0.54290236151
 
0.5%
0.21478746091
 
0.5%
0.19789365381
 
0.5%
0.79896323221
 
0.5%
0.83047253891
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.066646754721
0.5%
0.087093041741
0.5%
0.1039359941
0.5%
0.19051460181
0.5%
0.19789365381
0.5%
0.20148341051
0.5%
0.20202933741
0.5%
0.21478746091
0.5%
0.22743417641
0.5%
0.22879698621
0.5%
ValueCountFrequency (%)
0.83126568471
0.5%
0.83107211091
0.5%
0.83047253891
0.5%
0.83026234881
0.5%
0.82941426471
0.5%
0.82638935821
0.5%
0.82617955641
0.5%
0.82518056781
0.5%
0.82312249171
0.5%
0.82297715211
0.5%

constitutional_time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct99
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.20725389
Minimum11
Maximum233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:39.048106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile27
Q147
median62
Q3103
95-th percentile205.6
Maximum233
Range222
Interquartile range (IQR)56

Descriptive statistics

Standard deviation57.59212321
Coefficient of variation (CV)0.66806586
Kurtosis0.1093821862
Mean86.20725389
Median Absolute Deviation (MAD)21
Skewness1.139208249
Sum16638
Variance3316.852655
MonotonicityNot monotonic
2022-06-15T15:56:40.805238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
629
 
4.7%
608
 
4.1%
446
 
3.1%
755
 
2.6%
315
 
2.6%
435
 
2.6%
585
 
2.6%
415
 
2.6%
475
 
2.6%
615
 
2.6%
Other values (89)135
69.9%
ValueCountFrequency (%)
111
 
0.5%
141
 
0.5%
191
 
0.5%
201
 
0.5%
241
 
0.5%
251
 
0.5%
262
1.0%
274
2.1%
282
1.0%
293
1.6%
ValueCountFrequency (%)
2331
0.5%
2312
1.0%
2271
0.5%
2211
0.5%
2141
0.5%
2131
0.5%
2111
0.5%
2091
0.5%
2081
0.5%
2041
0.5%

first_regime_time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct80
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.79792746
Minimum1
Maximum233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:42.428238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median27
Q348
95-th percentile110.2
Maximum233
Range232
Interquartile range (IQR)34

Descriptive statistics

Standard deviation39.51879294
Coefficient of variation (CV)1.045528038
Kurtosis7.385515371
Mean37.79792746
Median Absolute Deviation (MAD)16
Skewness2.469340765
Sum7295
Variance1561.734996
MonotonicityNot monotonic
2022-06-15T15:56:44.110239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177
 
3.6%
147
 
3.6%
166
 
3.1%
46
 
3.1%
35
 
2.6%
195
 
2.6%
315
 
2.6%
55
 
2.6%
155
 
2.6%
285
 
2.6%
Other values (70)137
71.0%
ValueCountFrequency (%)
15
2.6%
23
1.6%
35
2.6%
46
3.1%
55
2.6%
64
2.1%
71
 
0.5%
83
1.6%
93
1.6%
103
1.6%
ValueCountFrequency (%)
2331
0.5%
2081
0.5%
2011
0.5%
1911
0.5%
1701
0.5%
1651
0.5%
1541
0.5%
1471
0.5%
1391
0.5%
1211
0.5%

1st_2nd_tfidf_adj
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.06468672845
Minimum0.000502996
Maximum0.907096975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:45.735766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.000502996
5-th percentile0.004316380572
Q10.01068151816
median0.02548487012
Q30.0555340769
95-th percentile0.2378890005
Maximum0.907096975
Range0.906593979
Interquartile range (IQR)0.04485255874

Descriptive statistics

Standard deviation0.131293906
Coefficient of variation (CV)2.029688456
Kurtosis26.298727
Mean0.06468672845
Median Absolute Deviation (MAD)0.01663822712
Skewness4.776623824
Sum8.409274699
Variance0.01723808975
MonotonicityNot monotonic
2022-06-15T15:56:47.165823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04420381
 
0.5%
0.010937462221
 
0.5%
0.0491424361
 
0.5%
0.0162906121
 
0.5%
0.030865015861
 
0.5%
0.01000031
 
0.5%
0.032293035831
 
0.5%
0.024296611671
 
0.5%
0.026115404841
 
0.5%
0.042761241
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.0005029961
0.5%
0.00080177045451
0.5%
0.002017331
0.5%
0.0023505043171
0.5%
0.0037944208331
0.5%
0.0040422785711
0.5%
0.004069670271
0.5%
0.0046179153851
0.5%
0.004658011251
0.5%
0.00507211
0.5%
ValueCountFrequency (%)
0.9070969751
0.5%
0.903648351
0.5%
0.551954151
0.5%
0.352314981
0.5%
0.3227149111
0.5%
0.316280561
0.5%
0.23799571
0.5%
0.237758591
0.5%
0.2276244761
0.5%
0.206510881
0.5%

1st_2nd_lda_adj
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.04951300263
Minimum3.557230182 × 10-5
Maximum0.8016318293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:48.684828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.557230182 × 10-5
5-th percentile0.006485400323
Q10.01182460787
median0.02055020617
Q30.04148586937
95-th percentile0.1757328853
Maximum0.8016318293
Range0.801596257
Interquartile range (IQR)0.02966126151

Descriptive statistics

Standard deviation0.1071385071
Coefficient of variation (CV)2.163845888
Kurtosis35.14639059
Mean0.04951300263
Median Absolute Deviation (MAD)0.01088323906
Skewness5.585072789
Sum6.436690342
Variance0.01147865971
MonotonicityNot monotonic
2022-06-15T15:56:50.089821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.061275493461
 
0.5%
0.011797379211
 
0.5%
0.036690555061
 
0.5%
0.0052357492491
 
0.5%
0.019654690161
 
0.5%
0.017179737121
 
0.5%
0.0076563376351
 
0.5%
0.018170248051
 
0.5%
0.020816817191
 
0.5%
0.044394790051
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
3.557230182 × 10-51
0.5%
0.0026200966891
0.5%
0.0031804203361
0.5%
0.0040334666841
0.5%
0.0052357492491
0.5%
0.0061743617311
0.5%
0.0064779548311
0.5%
0.0064945003681
0.5%
0.006499780571
0.5%
0.0067620877261
0.5%
ValueCountFrequency (%)
0.80163182931
0.5%
0.7773782541
0.5%
0.40771436861
0.5%
0.23288617031
0.5%
0.19697381141
0.5%
0.1874375041
0.5%
0.1790470861
0.5%
0.17168219551
0.5%
0.14903370431
0.5%
0.14582445481
0.5%

1st_2nd_use_adj
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.01530081815
Minimum0.0002758478593
Maximum0.3867030993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:51.261825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0002758478593
5-th percentile0.000782763203
Q10.001691636004
median0.003519769878
Q30.009301449275
95-th percentile0.04838330805
Maximum0.3867030993
Range0.3864272515
Interquartile range (IQR)0.00760981327

Descriptive statistics

Standard deviation0.04763135418
Coefficient of variation (CV)3.11299394
Kurtosis40.10922311
Mean0.01530081815
Median Absolute Deviation (MAD)0.002457200481
Skewness6.089131799
Sum1.989106359
Variance0.002268745901
MonotonicityNot monotonic
2022-06-15T15:56:52.283823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0068904117321
 
0.5%
0.0014165769331
 
0.5%
0.016429580161
 
0.5%
0.00031104886091
 
0.5%
0.0014541637251
 
0.5%
0.002385951711
 
0.5%
0.002197302841
 
0.5%
0.002192817461
 
0.5%
0.0088909917331
 
0.5%
0.020540126621
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.00027584785931
0.5%
0.00031104886091
0.5%
0.0003468803761
0.5%
0.00070090876381
0.5%
0.00071754416931
0.5%
0.00073658458311
0.5%
0.0007792561
0.5%
0.00078704978451
0.5%
0.00078822534431
0.5%
0.000863962361
0.5%
ValueCountFrequency (%)
0.38670309931
0.5%
0.30057658421
0.5%
0.21583030061
0.5%
0.10536137011
0.5%
0.087503944891
0.5%
0.05530423281
0.5%
0.052389830321
0.5%
0.043486447511
0.5%
0.031956854451
0.5%
0.028349480511
0.5%

1st_2nd_stm_adj
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.05353556076
Minimum0.001682788181
Maximum0.7744447283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:53.625821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.001682788181
5-th percentile0.005117488186
Q10.01071727296
median0.02048012628
Q30.04495070752
95-th percentile0.1943520418
Maximum0.7744447283
Range0.7727619401
Interquartile range (IQR)0.03423343456

Descriptive statistics

Standard deviation0.1151493406
Coefficient of variation (CV)2.150894452
Kurtosis26.22181773
Mean0.05353556076
Median Absolute Deviation (MAD)0.01162726414
Skewness4.89588739
Sum6.959622899
Variance0.01325937065
MonotonicityNot monotonic
2022-06-15T15:56:54.833347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.022601182921
 
0.5%
0.0094495732541
 
0.5%
0.039635345331
 
0.5%
0.0074219281131
 
0.5%
0.0037461197511
 
0.5%
0.091710211141
 
0.5%
0.0059446177411
 
0.5%
0.039518176531
 
0.5%
0.016022148291
 
0.5%
0.050905829311
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.0016827881811
0.5%
0.0021653332081
0.5%
0.0026883318471
0.5%
0.002764906081
0.5%
0.0036571651311
0.5%
0.0037461197511
0.5%
0.0047101182171
0.5%
0.0056153848141
0.5%
0.0059408705851
0.5%
0.0059446177411
0.5%
ValueCountFrequency (%)
0.77444472831
0.5%
0.76367544621
0.5%
0.61414533751
0.5%
0.34771308751
0.5%
0.24355851831
0.5%
0.21791508461
0.5%
0.20837550941
0.5%
0.17721224811
0.5%
0.17558167021
0.5%
0.15589683771
0.5%

1st_curr_tfidf_adj
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.007589426288
Minimum0.0003896447887
Maximum0.02386050154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:56.160357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0003896447887
5-th percentile0.001023223277
Q10.003846985604
median0.00646586775
Q30.009864114097
95-th percentile0.01796670161
Maximum0.02386050154
Range0.02347085675
Interquartile range (IQR)0.006017128493

Descriptive statistics

Standard deviation0.005316036545
Coefficient of variation (CV)0.7004530176
Kurtosis0.6404123376
Mean0.007589426288
Median Absolute Deviation (MAD)0.003069252825
Skewness1.019513985
Sum0.9866254175
Variance2.826024455 × 10-5
MonotonicityNot monotonic
2022-06-15T15:56:57.392346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0030658262631
 
0.5%
0.0042340457631
 
0.5%
0.0043297300441
 
0.5%
0.0045345874391
 
0.5%
0.011920016611
 
0.5%
0.0038629661291
 
0.5%
0.0064341864861
 
0.5%
0.0021906454551
 
0.5%
0.0044098822251
 
0.5%
0.013110726431
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.00038964478871
0.5%
0.00070498351
0.5%
0.00072327325581
0.5%
0.00075304912161
0.5%
0.00084055416671
0.5%
0.00094319833331
0.5%
0.00097646862071
0.5%
0.0010803678571
0.5%
0.0012937295451
0.5%
0.0014971601791
0.5%
ValueCountFrequency (%)
0.023860501541
0.5%
0.022727272731
0.5%
0.022673309581
0.5%
0.021969011831
0.5%
0.020012699311
0.5%
0.019121791
0.5%
0.018043729181
0.5%
0.017872556811
0.5%
0.017567871921
0.5%
0.016004732561
0.5%

1st_curr_lda_adj
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.007443657467
Minimum0.0002095214932
Maximum0.02457363487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:56:58.748352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0002095214932
5-th percentile0.002548282778
Q10.003920876611
median0.006895408428
Q30.01036407592
95-th percentile0.0136626849
Maximum0.02457363487
Range0.02436411338
Interquartile range (IQR)0.006443199308

Descriptive statistics

Standard deviation0.004101948869
Coefficient of variation (CV)0.5510663121
Kurtosis1.502641198
Mean0.007443657467
Median Absolute Deviation (MAD)0.003108997307
Skewness0.9198687379
Sum0.9676754708
Variance1.682598452 × 10-5
MonotonicityNot monotonic
2022-06-15T15:57:00.030346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0064055405841
 
0.5%
0.004580270591
 
0.5%
0.0032326480231
 
0.5%
0.0040978494191
 
0.5%
0.011986502911
 
0.5%
0.0067223118771
 
0.5%
0.0030491245631
 
0.5%
0.000723306931
 
0.5%
0.0039770920821
 
0.5%
0.01069619951
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.00020952149321
0.5%
0.000723306931
0.5%
0.0011142189531
0.5%
0.0017761413991
0.5%
0.0023199898071
0.5%
0.0024637058941
0.5%
0.002541676891
0.5%
0.0025563566421
0.5%
0.0025815080161
0.5%
0.0025817338671
0.5%
ValueCountFrequency (%)
0.024573634871
0.5%
0.01891854541
0.5%
0.018428349981
0.5%
0.016164586411
0.5%
0.014468061311
0.5%
0.013990081421
0.5%
0.013749567571
0.5%
0.013556494971
0.5%
0.013268029721
0.5%
0.012875945891
0.5%

1st_curr_use_adj
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.001763425116
Minimum8.434639675 × 10-5
Maximum0.008277623793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:57:01.259351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.434639675 × 10-5
5-th percentile0.0002272789074
Q10.0008684663079
median0.001311715606
Q30.002135890599
95-th percentile0.004904710261
Maximum0.008277623793
Range0.008193277397
Interquartile range (IQR)0.001267424291

Descriptive statistics

Standard deviation0.001500057454
Coefficient of variation (CV)0.8506499318
Kurtosis4.066056521
Mean0.001763425116
Median Absolute Deviation (MAD)0.0006193336455
Skewness1.927897491
Sum0.229245265
Variance2.250172366 × 10-6
MonotonicityNot monotonic
2022-06-15T15:57:02.096350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0016865054961
 
0.5%
0.00073621845591
 
0.5%
0.0014475401021
 
0.5%
0.00052954946241
 
0.5%
0.0024393344851
 
0.5%
0.0011558902641
 
0.5%
0.0012334482551
 
0.5%
0.00013589440321
 
0.5%
0.0024066818291
 
0.5%
0.001438365491
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
8.434639675 × 10-51
0.5%
0.00012426415611
0.5%
0.00013589440321
0.5%
0.00016460190351
0.5%
0.00018182641
0.5%
0.00021644329581
0.5%
0.00022280543481
0.5%
0.0002327464851
0.5%
0.00040246916661
0.5%
0.00042324249441
0.5%
ValueCountFrequency (%)
0.0082776237931
0.5%
0.0070460053581
0.5%
0.006498115181
0.5%
0.0060987612691
0.5%
0.0055241540851
0.5%
0.0052857937851
0.5%
0.0049307872871
0.5%
0.0048728383391
0.5%
0.0048701500681
0.5%
0.0047248275621
0.5%

1st_curr_stm_adj
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct130
Distinct (%)100.0%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.006985924078
Minimum0.0006883178409
Maximum0.01888800252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:57:03.010349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0006883178409
5-th percentile0.002305444263
Q10.003935651623
median0.006695388051
Q30.008977120081
95-th percentile0.01345980475
Maximum0.01888800252
Range0.01819968468
Interquartile range (IQR)0.005041468458

Descriptive statistics

Standard deviation0.003829646204
Coefficient of variation (CV)0.5481946499
Kurtosis0.5827017658
Mean0.006985924078
Median Absolute Deviation (MAD)0.002743480786
Skewness0.9001967536
Sum0.9081701301
Variance1.466619005 × 10-5
MonotonicityNot monotonic
2022-06-15T15:57:04.083353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0035500804131
 
0.5%
0.0055924807621
 
0.5%
0.003492100911
 
0.5%
0.0048310912821
 
0.5%
0.013407511041
 
0.5%
0.0087564897011
 
0.5%
0.0029025332561
 
0.5%
0.0029983886931
 
0.5%
0.0038227905851
 
0.5%
0.011072967191
 
0.5%
Other values (120)120
62.2%
(Missing)63
32.6%
ValueCountFrequency (%)
0.00068831784091
0.5%
0.00098456353441
0.5%
0.0015499245281
0.5%
0.0015552328881
0.5%
0.0019051460181
0.5%
0.0021961254011
0.5%
0.0022735999351
0.5%
0.0023443651091
0.5%
0.0026267966981
0.5%
0.0028330152421
0.5%
ValueCountFrequency (%)
0.018888002521
0.5%
0.018869598841
0.5%
0.018114482671
0.5%
0.015909187341
0.5%
0.015721220681
0.5%
0.013527550291
0.5%
0.01350259051
0.5%
0.013407511041
0.5%
0.013071420271
0.5%
0.012912385361
0.5%

tfidf_distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct131
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.064712286
Minimum0
Maximum8.805528793
Zeros63
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:57:05.217352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.6192621589
Q31.457402639
95-th percentile4.336997896
Maximum8.805528793
Range8.805528793
Interquartile range (IQR)1.457402639

Descriptive statistics

Standard deviation1.432658755
Coefficient of variation (CV)1.345583003
Kurtosis5.270935841
Mean1.064712286
Median Absolute Deviation (MAD)0.6192621589
Skewness2.054736816
Sum205.4894711
Variance2.052511108
MonotonicityNot monotonic
2022-06-15T15:57:06.250350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063
32.6%
2.554667161
 
0.5%
1.3462370041
 
0.5%
0.98284872061
 
0.5%
3.6804614661
 
0.5%
2.8663236281
 
0.5%
0.36757779121
 
0.5%
1.500623421
 
0.5%
0.35331201551
 
0.5%
2.6273455921
 
0.5%
Other values (121)121
62.7%
ValueCountFrequency (%)
063
32.6%
0.020173311231
 
0.5%
0.031100749971
 
0.5%
0.056591928011
 
0.5%
0.056924104691
 
0.5%
0.082994341851
 
0.5%
0.087367415431
 
0.5%
0.12006580831
 
0.5%
0.12809246781
 
0.5%
0.13836026191
 
0.5%
ValueCountFrequency (%)
8.8055287931
0.5%
5.9261863461
0.5%
5.6656357051
0.5%
5.3635898681
0.5%
4.7146407071
0.5%
4.6143610331
0.5%
4.5633090591
0.5%
4.488095631
0.5%
4.3988499341
0.5%
4.3703974781
0.5%

lda_distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct131
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8243181345
Minimum0
Maximum5.310201276
Zeros63
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:57:07.231349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5798510483
Q31.150007772
95-th percentile2.687808073
Maximum5.310201276
Range5.310201276
Interquartile range (IQR)1.150007772

Descriptive statistics

Standard deviation0.9664345238
Coefficient of variation (CV)1.172404783
Kurtosis3.256722106
Mean0.8243181345
Median Absolute Deviation (MAD)0.5798510483
Skewness1.675308658
Sum159.0934
Variance0.9339956887
MonotonicityNot monotonic
2022-06-15T15:57:08.240349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063
32.6%
2.1824149691
 
0.5%
1.1357373411
 
0.5%
0.73381110111
 
0.5%
2.5985927621
 
0.5%
1.3521631961
 
0.5%
0.54554806281
 
0.5%
0.30113395981
 
0.5%
0.23679533351
 
0.5%
2.3419690531
 
0.5%
Other values (121)121
62.7%
ValueCountFrequency (%)
063
32.6%
0.0090094242091
 
0.5%
0.1065684841
 
0.5%
0.11365033321
 
0.5%
0.231700491
 
0.5%
0.23679533351
 
0.5%
0.27030336661
 
0.5%
0.27036661811
 
0.5%
0.27834590331
 
0.5%
0.28473155181
 
0.5%
ValueCountFrequency (%)
5.3102012761
0.5%
4.2996766021
0.5%
3.9816888471
0.5%
3.6084187391
0.5%
3.5184761251
0.5%
3.4228462271
0.5%
3.1520029941
0.5%
3.0266613641
0.5%
3.0145112131
0.5%
2.8131275731
0.5%

use_distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct131
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.177404477
Minimum0
Maximum1.839643225
Zeros63
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:57:09.342348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.08428697711
Q30.242137435
95-th percentile0.7334038791
Maximum1.839643225
Range1.839643225
Interquartile range (IQR)0.242137435

Descriptive statistics

Standard deviation0.2513756453
Coefficient of variation (CV)1.416963368
Kurtosis10.67329134
Mean0.177404477
Median Absolute Deviation (MAD)0.08428697711
Skewness2.667428981
Sum34.23906407
Variance0.06318971504
MonotonicityNot monotonic
2022-06-15T15:57:10.221347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063
32.6%
0.27216371961
 
0.5%
0.098024937141
 
0.5%
0.32859160331
 
0.5%
0.28564375671
 
0.5%
0.1224333671
 
0.5%
0.079739117991
 
0.5%
0.084286977111
 
0.5%
0.022950661441
 
0.5%
0.50798928521
 
0.5%
Other values (121)121
62.7%
ValueCountFrequency (%)
063
32.6%
0.0053433587131
 
0.5%
0.0109095841
 
0.5%
0.022950661441
 
0.5%
0.024834149061
 
0.5%
0.025709678741
 
0.5%
0.032101794731
 
0.5%
0.033643620661
 
0.5%
0.037538799251
 
0.5%
0.038992713191
 
0.5%
ValueCountFrequency (%)
1.8396432251
0.5%
0.99814962391
0.5%
0.97617817191
0.5%
0.92599649361
0.5%
0.89767170331
0.5%
0.81009060491
0.5%
0.80507381491
0.5%
0.77837980981
0.5%
0.74246582861
0.5%
0.74042418891
0.5%

stm_distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct131
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.131337558
Minimum0
Maximum13.761275
Zeros63
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-06-15T15:57:10.994350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5333420282
Q31.453148717
95-th percentile4.325649139
Maximum13.761275
Range13.761275
Interquartile range (IQR)1.453148717

Descriptive statistics

Standard deviation1.739749441
Coefficient of variation (CV)1.537781035
Kurtosis15.77721491
Mean1.131337558
Median Absolute Deviation (MAD)0.5333420282
Skewness3.237533468
Sum218.3481487
Variance3.026728118
MonotonicityNot monotonic
2022-06-15T15:57:11.810419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063
32.6%
1.8524200441
 
0.5%
1.5590170851
 
0.5%
0.79270690661
 
0.5%
4.2115837061
 
0.5%
2.5771733161
 
0.5%
1.4200036921
 
0.5%
0.35745828671
 
0.5%
0.43500271291
 
0.5%
3.3614021
 
0.5%
Other values (121)121
62.7%
ValueCountFrequency (%)
063
32.6%
0.066646754721
 
0.5%
0.1039359941
 
0.5%
0.24938909691
 
0.5%
0.25549717271
 
0.5%
0.25644072471
 
0.5%
0.28533249781
 
0.5%
0.28846524411
 
0.5%
0.30303842461
 
0.5%
0.3240621721
 
0.5%
ValueCountFrequency (%)
13.7612751
0.5%
7.6551794791
0.5%
7.0117779331
0.5%
6.5482532061
0.5%
6.4693610331
0.5%
5.6230320881
0.5%
5.458061281
0.5%
5.2905150121
0.5%
4.5258695251
0.5%
4.4967472881
0.5%

Interactions

2022-06-15T15:55:03.676945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:46.320725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:57.672058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:09.462144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:19.764691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:28.256695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:37.380689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:55.581855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:15.326632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:35.160312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:53.417887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:16.070451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:37.232482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:01.494412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:25.894568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:53.744521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:17.582434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:38.859689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:04.937750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:33.312414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:02.551331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:29.373891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:54.220525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:54:27.602501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:55:04.997001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:47.200723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:58.064054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:09.825691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:20.068890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:28.589689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:37.822695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:56.272981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:15.808701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:35.640311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:54.203883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:16.924066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:38.225093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:02.142413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:27.460572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:55.074561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:18.479431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:39.767687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:05.804810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:34.402955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:03.585408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:30.210648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:55.120596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:54:29.171193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:55:06.013998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:47.778699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:58.419057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:10.306697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:20.386690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:28.891693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:38.239698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:56.893985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:16.625230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:36.205315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:55.051976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:17.522052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:39.003097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:02.870988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:28.811569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:56.122574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:19.358968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:40.720220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:07.027866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:35.550010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:04.988482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:31.034176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:56.163599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:54:30.761373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:55:07.429198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:48.259350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:58.858058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:10.853691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:20.777942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:29.201696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:38.606694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:57.649983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:17.414236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:36.818315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:55.761984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:18.246173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:39.984614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:03.633472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:30.122569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:57.284465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:22.371147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:41.796278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:08.500398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:37.052015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:06.104488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:31.971169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:57.142595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:54:32.132376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:55:08.741730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:48.708465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:59.317124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:11.267691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:21.156689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:29.482695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:38.944694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:58.422513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:18.031234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:37.574314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:56.435982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:19.277175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:40.996611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:04.450010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:31.438667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:58.132470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:23.042146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:42.580820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:09.556394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:38.140012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:07.301009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:33.087174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:57.999594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:54:33.407437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:55:10.065729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:49.011346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:59.679175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:11.900690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-06-15T15:53:26.536197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:51.499302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:54:22.032432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:54:58.497173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:55:29.875631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:56.962064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:08.596144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:19.013688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:27.512690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:36.274694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:54.455254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:14.439393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:33.733315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:51.790391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:13.859380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:35.341318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:59.773160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:22.716568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:51.104985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:15.491433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:36.676691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:03.238748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:30.720244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:00.634964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:27.320203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:52.424458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:54:23.731017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:55:00.208416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:55:30.925628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:46:57.314060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:09.029144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:19.393689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:27.892691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:36.794690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:47:55.083856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:14.865473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:34.561317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:48:52.675631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:15.006460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:49:36.239483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:00.675261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:24.223569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:50:52.294522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:16.504441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:51:37.892691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:04.076752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:52:31.918419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:01.618108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:28.222730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:53:53.375527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:54:25.336017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-15T15:55:01.688419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-15T15:57:12.943417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-15T15:57:14.550421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-15T15:57:16.707576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-15T15:57:19.419155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-15T15:55:33.264159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-15T15:55:43.069136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-15T15:55:48.273120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-15T15:55:54.083472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

codecountry1st_const_year2nd_const_year1st_2nd_tfidf1st_current_tfidf1st_2nd_lda1st_current_lda1st_2nd_use1st_current_use1st_2nd_stm1st_current_stmconstitutional_timefirst_regime_time1st_2nd_tfidf_adj1st_2nd_lda_adj1st_2nd_use_adj1st_2nd_stm_adj1st_curr_tfidf_adj1st_curr_lda_adj1st_curr_use_adj1st_curr_stm_adjtfidf_distancelda_distanceuse_distancestm_distance
0AFGAfghanistan19231931.00.3536300.3035170.4902040.6341490.0551230.1669640.1808090.351458998.00.0442040.0612750.0068900.0226010.0030660.0064060.0016870.0035502.5546672.1824150.2721641.852420
1ALBAlbania19251928.00.5134470.6363410.4374730.7886220.0828780.1021030.5267450.751644973.00.1711490.1458240.0276260.1755820.0065600.0081300.0010530.0077492.8180892.4124500.7783803.296292
2DZAAlgeria19631996.00.6509470.6509470.5368400.5368400.1329690.1329690.4839080.4839085933.00.0197260.0162680.0040290.0146640.0110330.0090990.0022540.0082020.6509470.5368400.1329690.483908
3ANDAndorra1993NaNNaNNaNNaNNaNNaNNaNNaNNaN2929.0NaNNaNNaNNaNNaNNaNNaNNaN0.0000000.0000000.0000000.000000
4AGOAngola19752010.00.3174430.3174430.5716230.5716230.3054110.3054110.4893180.4893184735.00.0090700.0163320.0087260.0139810.0067540.0121620.0064980.0104110.3174430.5716230.3054110.489318
5ATGAntigua Barbuda1981NaNNaNNaNNaNNaNNaNNaNNaNNaN4141.0NaNNaNNaNNaNNaNNaNNaNNaN0.0000000.0000000.0000000.000000
6ARGArgentina18191826.00.5054890.7943050.2517270.7332570.0145850.0439380.2528340.4615412037.00.0722130.0359610.0020840.0361190.0039130.0036120.0002160.0022741.1499670.8812070.0375390.714374
7ARMArmenia19952005.00.0645110.3719610.1224430.3777320.0342810.0963560.1804140.3529282710.00.0064510.0122440.0034280.0180410.0137760.0139900.0035690.0130710.3890740.4190850.0840170.533342
8AUSAustralia1901NaNNaNNaNNaNNaNNaNNaNNaNNaN121121.0NaNNaNNaNNaNNaNNaNNaNNaN0.0000000.0000000.0000000.000000
9AUTAustria19201934.00.3382170.3382170.1136500.1136500.0489030.0489030.4032900.40329010214.00.0241580.0081180.0034930.0288060.0033160.0011140.0004790.0039540.3382170.1136500.0489030.403290

Last rows

codecountry1st_const_year2nd_const_year1st_2nd_tfidf1st_current_tfidf1st_2nd_lda1st_current_lda1st_2nd_use1st_current_use1st_2nd_stm1st_current_stmconstitutional_timefirst_regime_time1st_2nd_tfidf_adj1st_2nd_lda_adj1st_2nd_use_adj1st_2nd_stm_adj1st_curr_tfidf_adj1st_curr_lda_adj1st_curr_use_adj1st_curr_stm_adjtfidf_distancelda_distanceuse_distancestm_distance
183USAUnited states1789NaNNaNNaNNaNNaNNaNNaNNaNNaN233233.0NaNNaNNaNNaNNaNNaNNaNNaN0.0000000.0000000.0000000.000000
184URYUruguay18301918.00.0705560.8528280.2305690.6612820.0242750.2017720.2365730.58216119288.00.0008020.0026200.0002760.0026880.0044420.0034440.0010510.0030321.2790920.9278960.1712291.410233
185UZBUzbekistan1992NaNNaNNaNNaNNaNNaNNaNNaNNaN3030.0NaNNaNNaNNaNNaNNaNNaNNaN0.0000000.0000000.0000000.000000
186VUTVanuatu19791980.00.0311010.0311010.0090090.0090090.0053430.0053430.0666470.066647431.00.0311010.0090090.0053430.0666470.0007230.0002100.0001240.0015500.0311010.0090090.0053430.066647
187VENVenezuela18301858.00.4953110.6903890.1818460.7608600.0471120.1850520.1842050.78007819228.00.0176900.0064950.0016830.0065790.0035960.0039630.0009640.0040635.6656363.6084190.50370413.761275
188VDRVietnam19601980.00.8855660.8914620.2400320.2632290.0526520.0569590.2534920.2287976220.00.0442780.0120020.0026330.0126750.0143780.0042460.0009190.0036901.1573570.4825460.0712020.482289
189YEMYemen Arab Republic19701991.00.9135290.9135290.6475010.6475010.0655790.0655790.6631850.6631855221.00.0435010.0308330.0031230.0315800.0175680.0124520.0012610.0127540.9135290.6475010.0655790.663185
190YUGYugoslavia19211931.00.7365630.9292970.4840820.7317030.0354650.1333980.4537620.68718810110.00.0736560.0484080.0035460.0453760.0092010.0072450.0013210.0068044.4880963.9816890.7424665.623032
191ZMBZambia19641973.00.4758260.0868920.1107000.1482690.0070830.0095470.0534680.202029589.00.0528700.0123000.0007870.0059410.0014980.0025560.0001650.0034830.9635510.2317000.0248340.255497
192ZWEZimbabwe19651969.00.2492810.9122700.3709430.8246790.0326590.0920310.3295740.688878574.00.0623200.0927360.0081650.0823930.0160050.0144680.0016150.0120861.1187741.4316190.1323591.478264